Nonlinear modeling simulation Optimization and control of Electrochemical chemical and Bioreactors

Abstract

Process engineers use fundamental scientific principles as the basis newlinefor mathematical models that characterize the behavior of a chemical process newlineSymbols are used to represent physical variables such as pressure newlineTemperature concentration or time Input information is specified and newlinenumerical algorithms are used to solve the models Process engineers analyze newlinethe results of these simulations to make decisions or recommendations newlineregarding the design operation and control of a process newlineMany of the chemical engineering processes are nonlinear For newlineSome fundamental models based on known physical chemical relationships newlineare available But if the process is too complex for a fundamental model an newlineempirical model which has satisfactory predictive capability of experimental newlinedata is developed Nonlinear empirical models of complex nonlinear processes like newlinephotoelectrocatalytic oxidation of textile dye effluent and electrooxidation of newlinedistillery effluent for which fundamental models are not available were newlinedeveloped using response surface methodology RSM and feed forward back newlinepropagation artificial neural network ANN to predict the experimental newlineresults and optimize the processes The treatment of procion blue dye effluent using a thin film photoelectrocatalytic novel reactor is reported RSM was applied to design the newlineexperiments and the optimum operating parameters were determined for newlinechemical oxygen demand COD removal and energy consumption Operating newlineparameters such as initial effluent concentration applied charge and lamp newlinewattage were selected The COD removal and energy required for treatment newlinewere optimized using RSM and a regression equation was developed for newlineCOD removal and energy consumption of photoelectrocatalytic process The newlinestudy concludes that the power consumption for the process can be optimized newlineusing RSM and that RSM is a good tool for studying combined variables and newlineinteraction effects on the response of a process newline newline